Computer-Assisted Translation: 2013

Tuesday, December 17, 2013

Reem Bazzal, and Nour Onies

Abstract In their text Evaluation of Machine Translation System and Human Translation with Literary texts (2013), Reem Bazzal, and Nour Onies intend to discuss the proper use of available online machine translation technologies in literary translation and provide an objective view of the suitability and reliability of these systems in translation. To illustrate more, practical text has been submitted and it was translated by both Machine Translation and Human Translation. They evaluated the work of such technologies and of human translation from four different aspects: semantics, syntax, morphology and comprehension and concluded that although Machine translation results are not that reliable, they can help in producing a well understood text. This paper is set out to verify this belief. Comprehension For a fair comparison of the performance of different online MT systems and Human Translation on literary texts, they needed appropriate text that the MT systems can evaluate. The text they choseThe Raven by Edgar Allan Poe is made up of 406 words in its English version. It is therefore a reliable test set for examining the translation quality of literary texts by MT systems and human translation and to produce substantial statistical analysis for their performance in quantitative evaluation. After translating the text by both human translation and machine translation which is google translate, we concluded the following: Semantics: When it comes to semantics, the meaning or the interpretation of a word or sentence we noticed that there are differences and similarities between the human translation and the machine translation. For example, in human translation, the expression midnight dreary was translated into ليلةٍ مقفرة while google translate translated it into ليلة كئيبة. Another example is the word pondered which in human translation meant تأملت while in machine translation it was translated to فكرت. “Rapping at my chamber door" was translated into “يقرع علىبابمخدعي" in human translation while with google translate it was translated to "موسيقىالرابفيبابحجرتي Syntax: Concerning the word order (syntax): the human translation is better than machine translation. The word combination in human translation is respected and well organized. It is more accurate and follows the grammatical rules: subject-verb and we can also notice the proper use of words such determiners, modifiers, and complements while in machine translation a majority of phrases started with the verb or the verb phrase instead of the noun. For example: Source sentence: And the silken sad uncertain rustling of each purple curtain Thrilled me Machine translation: وحريريسرقةمؤكدحزينةلكلستارارجواني Human translation: الحفيفُالحريريُّالحزينُوالغامضُلكلِّستارةٍأرجوانية أثارني Source sentence: While I nodded, nearly napping, suddenly there came a tapping Machine translation: بينماكنتضربةرأس،القيلولةتقريبا،فجأةهناكجاءالتنصت، Human translation: وعندماسقطرأسيعلىصدريوأوشكتُأنأغفو فجأةًجاءَالطرقُ Morphology: Concerning the morphology, machine translation has a problem when it comes to translation related to Inflectional morphemes, the exact number, gender of the subject and the exact verb. When translating from English to Arabic, the agreement between grammatically linked items was often missing which led to some sentences that can be hardly understood. While human translation has proved its efficiency in giving the exact verb tense number, possession, and comparison. For example: Source text: While I nodded, nearly napping, suddenly there came a tapping Human translation: وعندماسقطرأسيعلىصدريوأوشكتُأنأغفو Machine translation: بينماكنتضربةرأس،القيلولةتقريبا،فجأةهناكجاءالتنصت، Another example on the wrong morphological interpretation that was clearly stated in the translation process: Source text: As of someone gently rapping, rapping at my chamber door. Human translation: وكأنأحداًبرفقٍيقرعُ...يقرعُعلىبابِمخدعي Machine translation: اعتبارامنبعضواحدموسيقىالراببلطف،موسيقىالرابفيبابحجرتي After comparing the two methods of translation we noticed that the target texts in the machine translation and despite the mistakes can be understood. But the use of grammar and punctuation is not correct and the sentences are sometimes lengthy and complex. Also, MT produces some sentences that are unfortunately far from the meaning given in the source text. As for human translation, the texts we well understood, the meaning was clearly stated in the target language and the use of grammar and punctuation is correct. Take the following sentences as examples: Source sentence: Once upon a midnight dreary Machine Translation:مرةواحدةعندمنتصفالليلالكئيب Human Translation:فيليلةٍ مقفرةٍعندمنتصفالليل Source sentence: Over many a quaint and curious volume of forgotten lore Machine Translation:أكثرمنوحدةتخزينالعديدغريبةوغريبةمنالعلمالنسيان Human Translation مررتُبعديدٍ منالمعتقداتِالقديمةِالغريبة التيغطاهاألنسيان Source sentence: distinctly I remember it was in the bleak December Machine Translation:بوضوحتذكرت،أنهكانفيديسمبرالقابض Human Translation:أتذكّرُبوضوحٍ إنه ديسمبرالكثيب Source sentence: the rare and radiant maiden whom the angels name Lenore Machine Translation: النادرةالمتألقةالنادرةالذينلينوراسمالملائكة Human translation: لتلكالفتاةِ الرائعةِالمتألقة التيأسمتهاالملائكةُُلينور Recommendations It is important to include in MT engines special lexicon domains that can be used in literal translation, which offers quite a wide variety of lexicons if one accesses the MT sites directly. It is necessary to take into consideration that on-line MT engines are aimed at helping users deal with ephemeral literary texts and they help communication in many situations. 1. It is important to understand that machine translation has definitely improved the work of journalistic translation whether by the use of blogs, emails or portables like new Samsung phones (S. Translator) and IPhone phones( I translate) and the intelligent use of them led to the focus of the best human efforts where they should be. Machine translation like blogs and iPad and S. translator must be used an alternative to the expensive human translation that takes time and is usually unavailable when it is needed for communicating quickly and cheaply with people with whom we do not share a common language.

Ghina Khamis and Nour Al Oneis

Evaluation of Machine Translation Tools and Human Translation in Economic Translation Ghina Khamis and Nour Al Oneis Instructor: Dr Ghada Awada Computer-Assisted Translation TRNS301 Table of Contents : Abstract……………………………………………………………1 1- Commentary………………………………………….….....2 1.1 -Test Texts……………………………………….……..2 1.2-Evaluation……………………………………………....2 1.2.1- Semantics……………………………………..2 1.2.2- Morphology……………………………….….3 1.2.3- Syntax………………………………………...4 1.2.4- Pragmatics…………………………………....5 1.2.5- Comprehension…………………………….....6 2-Recommendations…………………………………………...7 3-Conclusion……………………………………………….......8 Terminology………………………………………………………9 References………………………………………………………..13 Abstract : In their evaluation of Human Translation and Machine Translation, Nour Al Oneis and Ghina Khamis (2013) studied and compared the differences between human’s and machine’s translation, defining the nature of translation and standards and criteria on which it is based, studying semantics, syntax, pragmatics, morphology and comprehension. This study aims to compare the effectiveness of the popular machine translation system (Google Translate) used to translate English sentences into Arabic relative to the effectiveness of English to Arabic human translation. This study will show that the Human Translation is better than Google machine translation system in terms of precision of translation from English to Arabic. This paper is set out to study the accuracy of this believe. 1-Commentary: 1.1 Test texts We performed the evaluation on two economical texts of the writer Alan Blanchette, the first entitled “Tax Avoidance” and the second “Money Laundering” , both published in 2010 and which has been reserved for testing. The texts have 504-636 words. All examples from the two texts are illustrated in that evaluation. 1.2- Evaluation: 1.2.1- Semantics: Semantic roles can determine how to properly express information in another language. Regarding the study of the meaning of linguistic expressions, it’s noteworthy to mention that many expressions and words differ in meaning between the human translation and the machine translation. Example: English العربية Human Machine catch مسك قبض Involves تشمل تتضمن wire transfers تحويلات برقية تحويلت سلكية changing the money's currency تغيير وضع العملة النقدية تغيير عملة النقود Layering involves sending the money التصنيف يتضمن ارسال الاموال التصفيف ينطوي على إرسال الأموال make it difficult to follow مما يجعل صعوبة في متابعته تجعل من الصعب متابعة But there were also similarities in the meaning in the two Translations, and it marked a kind of accuracy, and an example of this: Similarities English العربية Global financial markets الاسواق المالية العالمية Money laundering غسيل الاموال Bank-secrecy law قوانين السرية المصرفية Single scheme مخطط واحد Authorities سلطات Criminals مجرمون Embezzlers مختلسون High-value transactions معاملات ذات قيمة عالية High-value items سلع عالية القيمة The main stream economy التيار الرئيسي للاقتصاد Bank-reporting laws قوانين الابلاغ المصرفية foil money-laundering operations. احباط عمليات غسيل الاموال Drug traffickers مهربو المخدرات Financial transactions معاملات مالية Illegal money اموال غير شرعية 1.2.2- Morphology: Concerning the morphology, machine translation was ambiguous in handling word forms, achieving Number and Gender agreement, Identifying the case ending of words, and in the proper handling of pronouns, while human translation has proved its efficiency in giving the exact verb tense number, possession, and the proper gender agreement and comparison. For example, the following sentences show Morphological issues with agglutinative, fusion and complex word structure: Source Sentence: Banks usually lend money to persons who need it, for a specified interest. Google Translate: البنوك عادة إقراض المال إلى الأشخاص الذين يحتاجون إليها، لمصلحة معينة Human Translation : تقوم البنوك باعطاء القروض عادة للأشخاص الذين يحتاجون إليها، في مصلحة معينة At this stage we have to compare the outputs of Google Translate system with the Human Translation. In the first comparison we found that there is: البنوك“ Banks" الذيwho” and “يحتاجون need” are common with human translation but “إقراض المال” and “اعطاء القروض” differs. Source Sentence: The temporary closure of international banks because of the earthquake I Japan, also dented profits. Human translation: وتسبّب الزلزال المدمر الذي ضرب اليابان بالإقفال المؤقت للبنوك الدولية ، الأمر الذي أدّى إلى انخفاض هذه الأرباح Machine translation: إغلاق مؤقت ل بنوك دولي بسبب الزلزال أنا اليابان ، تراجع أيضا الأرباح. Source Sentence: Investors remain cautiously optimistic. Machine translation: ولكن لا يزال المستثمرون متفائلون بحذر . Human translation: إلا أن المستثمرين بقيوا متفائلبن بحذر 1.2.3- Syntax: Concerning syntax, a big difference in the combination of words appears, especially in dealing with Superlative Adjectives, and arranging Nouns and Adjectives and generating Nominal Chunks, hence, the human Translation syntax study is more accurate and follows the grammatical rules: Subject –Verb and we can also notice the proper use of words such determiner, modifier, complement. In the machine Translation a majority of phrases started with the verb or the verb phrase instead of the Noun, and the structure is most likely similar to the combination of words in the Arabic language, hence, the Arabic uses the: Verb – Subject – Object combinational structure while, in the English language, its : Subject-Verb + prepositional phrase. Phrase: NG (noun group), AdjG (adjective group), AdvG (adverb group), PG (prepositional group), PossG (possessive group). -In machine translation, the phrase started with a verb then subject: قد تتكون طبقات من عدة بنوك إلى بنك التحويلات - The usage of prepositions that differs in meaning, but not in structure: ومخطط واحد عادة ما ينطوي على تحويل الأموال من خلال العديد من البلدان- - والسبب في أنه من ضروري - الذي يغسل الأموال وكيف تفعل ذلك 3- the substitution of the preposition, but the reservation of the same structure and the same meaning :مما يجعل من الممكن إيداع مجهول الأموال "القذرة So the syntactic transfer rules that map parse tree for one language into one for another are: – English to Arabic: • NP → Adj Nom Þ NP → Nom ADJ • VP → V NP Þ VP → NP V • PP → P NP Þ PP → NP P 1.2.4- Pragmatics and Comprehension: Concerning Comprehension, the translation must transfer the meaning intended, which makes the comprehension part a major one. Yet, in machine translation, it is not always the case, it produces sometimes sentences, that unfortunately mean something different from the original source text. The majority of machine translation expressions have resulted into a literal and meaningless translation of the saying. For instance, the English say “A good workman is known by his chips”; which has the Arabic meaning as was literally mistranslated into “ومن المعروف ان العامل الجيد من قبل رقائقه”, by the Google translation; which is very literal and very far from the actual meaning of the saying. Source Sentence: The rise of global financial markets makes money laundering easier than ever. Machine Translation : صعود الأسواق المالية العالمية يجعل غسل الأموال أسهل من أي وقت مضى Human Translation : ارتفاع الاسواق العالمية يجعل غسل الاموال اسهل بكثير Source Sentence: In this article, we'll learn exactly what money laundering is and why it's necessary. Machine Translation: في هذه المقالة، وسوف تتعلم بالضبط ما هو غسيل الأموال والسبب في أنه من الضروري، الذي يغسل الأموال وكيف تفعل ذلك Human Translation: في هذه المقالة, سوف تتعلم بالتحديد ما هو غسيل الاموال و ما السبب في انه ضروري Source Sentence: At this stage, the launderer inserts the dirty money into a legitimate financial institution. Machine Translation : في هذه المرحلة، وغاسلي الأموال القذرة يدرج في مؤسسة مالية مشروعة. Human Translation : في هذه المرحلة, يضع غاسلوا الاموال اموالهم في مؤسسة مالية مشروعة Source Sentence: At the integration stage, the money re-enters the mainstream economy in legitimate-looking form. Machine Translation: في مرحلة التكامل، وإعادة الأموال يدخل-التيار الرئيسي للاقتصاد في شكل المشروعة المظهر Human Translation: في مرحلة التكامل, يتم اعادة ادخال الاموال للتيار الرئيسي للاقتصاد في شكل شرعي. According to these examples, we can discern the following differences in meanings: Differences English العربية Human Machine easier than ever اسهل بكثير أسهل من أي وقت the launderer inserts the dirty money , يضع غاسلوا الاموال اموالهم غاسلي الأموال القذرة يدرج the money re-enters , يتم اعادة ادخال الاموال وإعادة الأموال يدخل Hit تلقت تعرضت in legitimate-looking form في شكل شرعي. في شكل المشروعة المظهر We have found out that the overall translation precision for Google was 31,4% , if we considered that the total of words in the two articles is approximately 600 words (504-636 words), and that 188.4 words/ expressions/ morphemes were considered as inaccurate, So the degree of accuracy of the Machine Translation from English to Arabic is around 30%. 2- Recommendations: -It is necessary to improve Machine Translation either, by developing more sophisticated methods or by imposing certain restrictions on the input in order to get a good translation that is fluent, grammatically well structured and readable in the target language. - The translation machine systems (Google, Babylon, tarjim..) should be guiding International economics and should guarantee the fidel translation of documents for businesspeople, bankers, financier and other professionals. - It’s recommended improve new developed features for Translation machines, such as automatic multilingual dictionaries, that handle more support of basic research in computational linguistics. - It is necessary to provide Global Translation service for economic translation of the different types of documents (Accounting statements, balance sheets, auditor’s repots and economis statistics), and this through developed software that are specific to this domain - A Global translation service should provide high-quality translation for any information contained in economic articles, getting it across to the target audience, by including specialized terminology and a good numbering. (statistics sheet) 3-Conclusion: To sum up, English-to-Arabic machine translation has been a challenging research issue for many of the researchers in the field of Arabic Language Processing. In this study, we have concluded that the effectiveness of Machine Translation is limited, furthermore, human translation remains the most accurate and effective in translating technical texts that are related to different domain, as the economic domain . And the human Translation will always be distinct, to an important degree, from the ways in which the major Online Translating Machines as Google Translate, babyllon, etc.. translate a source text in a certain specific domain.. The human translation hence, stays in the first raw in being indispensable for translating data.

Inaam Kuteish and Maha Atwe

Abstract: In line with Inaam Kuteish and Maha Atwe (2013) This paper presents the differences between machine translation and human translation in the literary field. Literary translations are uniquely challenging because they require high-level understanding of specific terms in both the source and target language. In this paper we will tackle semantics, syntax, morphology, comprehensive and terminological differences. Also, we will shed the light on the ways of translating such documents and will show examples differentiating between translated documents. Recommendations are also given in this paper. Sample The Raven by Edgar Allan Poe Once upon a midnight dreary, while I pondered, weak and weary, Over many a quaint and curious volume of forgotten lore, While I nodded, nearly napping, suddenly there came a tapping, As of someone gently rapping, rapping at my chamber door. "'Tis some visitor," I muttered, "tapping at my chamber door - Only this, and nothing more." Ah, distinctly I remember it was in the bleak December, And each separate dying ember wrought its ghost upon the floor. Eagerly I wished the morrow; - vainly I had sought to borrow From my books surcease of sorrow - sorrow for the lost Lenore - For the rare and radiant maiden whom the angels name Lenore - Nameless here for evermore. And the silken sad uncertain rustling of each purple curtain Thrilled me - filled me with fantastic terrors never felt before; So that now, to still the beating of my heart, I stood repeating, "'Tis some visitor entreating entrance at my chamber door - Some late visitor entreating entrance at my chamber door; - This it is, and nothing more." Human translation الغراب شعر: أدغار ألن بو ترجمة: د. إنعام الهاشمي -------------------------------- - 1 - في ليلةٍ مقفرةٍ عند منتصف الليل ، حيث كنت أتأمل وأفكِّر، حائراً، متعباً وضجِر مررتُ بعديدٍ من المعتقداتِ القديمةِ الغريبة – التي غطاها ألنسيان وعندما سقط رأسي على صدري وأوشكتُ أن أغفو فجأةً جاءَ الطرقُ وكأن أحداً برفقٍ يقرعُ .. يقرعُ على بابِ مخدعي تمتمتُ "هو زائرٌ" "هو زائرٌ يقرعُ على بابِ مخدعي – هذا فقط... لا غير" ............................ - 2 - آه.. أتذكّرُ بوضوحٍ إنه ديسمبر الكثيب وفيه كلُّّ جذوةٍ تحتضرُ وتلقي بشبحِها يتمطّى فوقَ الأرض . بلهفةًٍ تمنيتُ الغد؛ – وبعبثٍ حاولتُ أن االتمسَ في كتُبي نهايةًً لهذا الأسى – ألأسى لفقدِ لينور – لتلك الفتاةِ الرائعةِ المتألقة التي أسمتها الملائكةُُ لينور – وهنا لا اسمَ لها ، ابداً بعد اليوم. ................... - 3 - الحفيفُ الحريريُّ الحزينُ والغامضُ لكلِّ ستارةٍ أرجوانية أثارني – ملأني برعبٍ غريب لم أعرفهُ أبدا من قبل ؛ والآن ، لأُهدِّئ دقاتِ قلبي ، وقفتُ أردِّد "هو أحدُ الزوّارِ يرجو الدخولَ في بابِ مخدعي "؛ – هو أحدُ زوّارِالليلِِ يرجو الدخولَ في بابِ مخدعي – هذا هو ، لا غير. Machine translation مرة واحدة عند منتصف الليل الكئيب، وأنا فكرت وضعيفة وأنهكته، أكثر من وحدة تخزين العديد غريبة وغريبة من العلم النسيان، بينما كنت ضربة رأس، القيلولة تقريبا، فجأة هناك جاء التنصت، اعتبارا من بعض واحد موسيقى الراب بلطف، موسيقى الراب في باب حجرتي. "'تيس بعض الزوار،" I تمتم "التنصت على باب حجرتي - هذا فقط، ولا شيء أكثر من ذلك. " آه، بوضوح تذكرت، أنه كان في ديسمبر القابض، ويحدثه كل جذوة مفصولة ميتة شكلت شبحا لها على الأرض. بلهفة تمنيت الغد، - عبثا التمست لاقتراض من surcease كتبي من الحزن - الحزن لفقدان ينور - لالبتول النادرة المتألقة النادرة الذين لينور اسم الملائكة - المجهولون هنا إلى الأبد. وحريري سرقة مؤكد حزينة لكل ستار ارجواني بسعادة غامرة لي - ملأني الرعب رائعة لم أحسها من قبل؛ بحيث الآن، لا يزال لضرب من قلبي، وقفت تكرار، "'تيس بعض الزوار تناشد مدخل عند باب حجرتي - بعض الزوار في وقت متأخر يستجدي مدخل عند باب حجرتي، - هذا هو عليه، وأكثر من ذلك لا شيء. " The article will tackle first Semantics: The word choice in machine translation is very weak where as the human translation which possesses an excellent word choice e.g.: "rapping at my chamber door" " يقرع على باب مخدعي"H.T: "موسيقى الراب في باب حجرتي "M.T: e.g.: "master" "سيد" H.T: "ماجستير"M.T: e.g.: "To the One in Paradise" "الى التي في الفردوس":H.T "الى واحد في الجنة"M.T: e.g.: Fluttering and dancing in the breeze H.T: تراقصُ النسيمَ وتناغي الوتر التصفيق والرقص في النسيم:M.T e.g.: unpurpeled الارجوانية H.T: M.T: unpurpled Syntax: The word order in machine translation is unorganized whereas the word order in human translation is well-organized. e.g.: "He wailed" H.T: "فرد متأسيا" M.T: "ناح انه" e.g.:This it is, and nothing more." H.T: هذا هو ، لا غير. M.T: هذا هو عليه، وأكثر من ذلك لا شيء. " e.g.: "'Tis some visitor," H.T: هو زائرٌ M.T: "'تيس بعض الزوار Morphology: Machine translation has weak grammar unlike human translation. ا E.g.: ", When all at once I saw a crowd" H.T:"فجأة وجدتُ جماعة" M.T:"في كل مرة عندما رأيت الحشد" E.g.: While I nodded, nearly napping, suddenly there came a tapping H.T: وعندما سقط رأسي على صدري وأوشكتُ أن أغفو فجأةً جاءَ الطرقُ M.T: بينما كنت ضربة رأس، القيلولة تقريبا، فجأة هناك جاء التنصت Comprehension: The comprehension level of machine translation is very low unlike the human translation. e.g.: "hon'ring" dids't"" "sent'st" "o'er" "upstarting" Quoth" H.T.: "تكريما اليك" " لم تفعلي" "ارجعته" " فوق" "منتفضاً" "قال " M.T: no translation was given e.g: surcease H.T.: نهايةً M.T: surcease Compatibility: The human translation is 90% compatible with the original text whereas the machine translation is 50% compatible. Analysis From here, we can find that the human translation is different than the machine translation; for the human translator has a mind and can comprehend and know how to make his article coherent and free of errors. The machine translated documents in the literary field are incoherent and lack the sense of comprehension and grammatical coherence. There are many differences between human translation and machine translation, in which errors regarding morphology, syntax, semantics, mechanics, coherence and comprehension are being found. The word choice in machine translation is very weak and the meaning is sometimes vague unlike the human translation which possesses an excellent word choice and uses exact terms that makes the meaning clear. The word order in machine translation is unorganized which makes the sentence barely readable whereas in human translation the word order is well-organized which makes the sentence very clear. Moreover, machine translation has weak grammar such as in sentence structure. . So in machine translation a lot of grammar mistakes are made while a human translator avoids the grammatical mistakes. The comprehension level of machine translation is very low because the machine translation doesn't comprehend the figurative language and the culture especially when it comes to proverbs and sayings unlike human translation. In addition, machine translation is unable to comprehend the old English language like the human translator. In human translation the original text is 90% compatible with the target text whereas in machine translation it is 50% compatible, the text might be explicable but barely readable. Recommendation It is imperative for translators to refrain from using machine translation. In case translators were obliged to seek help they can refer to specialized dictionaries, in addition we recommended to use tradius Moreover, if machine translation is used one should revise the whole text and check the mistakes. It is recommended for those who want to translate texts to seek translators and not machines because a translator can render the original text in an effective and clear way unlike the machine. Although some decoys can be found in the machine translated documents because of the structural changes, these are no more than 5% of the whole document, hence here comes the role of the human translator to detect the errors and correct them. Conclusion To sum up, machine translation has many disadvantages. Machine translation is not reliable; it doesn't give exact meanings, its word order is unorganized, it is weak in structure, it makes many grammar mistakes, and it lacks the ability to comprehend the whole text unlike a human translator who lacks all of these problems. To avoid these problems one should simply resort to human translation. But whenever the translator do his job in a way using his translating skills with the help of the machine, his job will be achieved perfectly without any errors or changes in the meaning. References Hashmy,I. (2011) . The raven. Retrieved on November 24, 2012 from http://www.almothaqaf.com/index.php?option=com_content&view=article&id=6529:2009-10-17-13-32-20&catid=35:2009-05-21-01-46-04&Itemid=0 Hashmy, I.(2011). The raven. Retrieved on July 20,2012 from http://www.babylon-center.net/?articles=topic&topic=556

Reem Bazzal, and Maha Atwi

Abstract In their text Machine Translation System and Human Translation with economic texts (2013), Reem Bazzal, and Maha Atwi intend to discuss the proper use of available online machine translation technologies in economic translation and provide an objective view of the suitability and reliability ofthese systems in translation. To illustrate more, practical texts have been submitted and they were translated by both Machine Translation and Human Translation. The evaluation ofthe work of such technologies and of human translation came based to four different aspects: semantics, syntax, morphology and comprehension and they concluded that although Machine translation results are not that reliable, they can help in producing a well understood text and help in finding some terms and expressions related to the economic field. This paper is set out to verify this belief. Commentary For a fair comparison of the performance of different online MT systems and Human Translation on economic texts, they needed appropriate texts that the MT systems can evaluate. The texts they chose Money Laundering is made up of 406 words in its English version and the other text Difference between Tax avoidance and Tax Evasion is made up of 243 words in its English version. They are therefore a reliable test set for examining the translation quality of economic texts by MT systems and human translation and to produce substantial statistical analysis for their performance in quantitative evaluation. After translating the two texts by a human translation and machine translation which is google translate, we concluded the following: Semantics: When it comes to semantics, the meaning or the interpretation of a word or sentence we noticed that there are differences and similarities between the human translation and the machine translation. For example, the human translation in the text money laundering translated the word: wire transfer as تحويلات برقية while the machine translated it as تحويلات سلكية . The word catch in human translation meant مسك while in machine translation it meant قبض. The phrase changing the money’s currency was translated in human translation to تغيير وضع العملة النقدية while in machine translation it was translated to تغيير عملة النقود . Despite the differences, machine translation and human translation had some words and expressions that hold the same meaning. For example, Global financial markets الاسواقالماليةالعالمية/ Money laundering غسيلالاموال/Bank-secrecy law قوانينالسريةالمصرفية/ Bank-reporting laws قوانينالابلاغالمصرفية. Morphology: Concerning the morphology, machine translation has a problem when it comes to translation related to Inflectional morphemes, the exact number, gender of the subject and the exact verb. When translating from Arabic to English, the agreement between grammatically linked items was often missing which led to some sentences that can be hardly understood. While human translation has proved its efficiency in giving the exact verb tense number, possession, and comparison. Source Sentence: The temporary closure of international banks because of the earthquake I Japan, also dented profits. Human translation: وتسبّبالزلزالالمدمرالذيضرباليابانبالإقفالالمؤقتللبنوكالدولية،الأمرالذيأدّىإلىانخفاضهذهالأرباح Machine translation: إغلاقمؤقتلبنوكدوليبسببالزلزالأنااليابان،تراجعأيضاالأرباح. Source Sentence: Investors remain cautiously optimistic. Machine translation: ولكنلايزالالمستثمرونمتفائلونبحذر . Human translation: إلاأنالمستثمرينبقيوامتفائلبنبحذر Syntax: Concerning the word order (syntax): the human translation is better than machine translation. The word combination in human translation is respected and well organized. It is more accurate and follows the grammatical rules: subject-verb and we can also notice the proper use of words such determiners, modifiers, and complements While in machine translation a majority of phrases started with the verb or the verb phrase instead of the noun, and the structure is most likely similar to the combination of words in the Arabic language, hence, the Arabic uses the : verb-subject-object combinational structure while in the English language, it is subject-verb-object- prepositional phrase (noun group, adjective, adverb ,prepositional, and possessive) so it’s obvious that google translate was unable to give good results for example: Comprehension: After comparing the two methods of translation we noticed that the target texts in the machine translation and despite the mistakes can be understood. But the use of grammar and punctuation is not correct and the sentences are sometimes lengthy and complex. Also, MT produces some sentences that are unfortunately far from the meaning given in the source text. As for human translation, the texts we well understood, the meaning was clearly stated in the target language and the use of grammar and punctuation is correct. Take the following sentences as examples: Source Sentence: The rise of global financial markets makes money laundering easier than ever. Machine Translation : صعودالأسواقالماليةالعالميةيجعلغسلالأموالأسهلمنأيوقتمضى Human Translation : ارتفاعالاسواقالعالميةيجعلغسلالاموالاسهلبكثير Source Sentence: At the integration stage, the money re-enters the mainstream economy in legitimate-looking form. Machine Translation: فيمرحلةالتكامل،وإعادةالأمواليدخل-التيارالرئيسيللاقتصادفيشكلالمشروعةالمظهر Human Translation: فيمرحلةالتكامل, يتماعادةادخالالاموالللتيارالرئيسيللاقتصادفيشكلشرعي. Recommendations: It is important to understand that machine translation is an interesting field of research but it is not a substitute for a professional translation produced by a human translator who is able to give exact meanings of the terms and expressions used. It is necessary to develop the machine translation that are related to specific domains so that terms related to economic or legal can be easily conducted and used. It is preferable to add translation memories to all machine translation to make sure that terms used are saved for the purpose of using it in the translation of other texts that are related to the same domain. It is necessary to improve the ability of the machine translation in focusing more on the production of perfect economic texts not only on the comprehension side. It is important to develop new machine translation methods that can help in creating well grammar links and understood sentences.

Reem Bazzal, and Maha Atwi

Wednesday, December 4, 2013

Journalistic Mobile Translation: Success or Failure?"

I. Abstract Mohammad, M., Farhat, A., Hamade, K., Hashem, R. (2013) in the paper entitled " Journalistic Mobile Translation: Success or Failure?" describes the technology available to translators in the first decade of the present century and examines the negative and positive aspects of mobile translation vs. human translation in the domain of journalism. The importance of this technology in discussed based on the idea of devices such as smartphones and tablets providing means for agencies to deliver language capabilities to users anywhere in the world. These ubiquitous devices can easily serve as platforms for translation programs, as a growing number of agencies are equipping personal with mobile devices and translation software that allow them to more efficiently complete their mission, especially as the arena of media deals with languages around the world. I. Commentary Introduction It is an undeniable fact that the globalization brings us to the modern effects such as the invention of mobile phones (MP). Just a couple years ago, we only had simple mobile phone’s application, such as VGA camera, a very limited scope of radio, and improved, in such a way that as a journalist, the target language can be obtained by simply vocally speaking. A- Endorsement Mobile translation is a machine translation service for hand-held devices, including mobile telephones, Pocket PCs, and PDAs. It relies on computer programming in the sphere of computational linguistics and the device's communication means (internet connection or SMS) to work. Such device users are equipped with the advantage instantaneous and non-mediated translation from one human language to another, usually against a service fee that is, nevertheless, significantly smaller that a human translator charges. In the chosen articles above, a significant and very basic role pg Google translation is noticed, that is, in this case the concise translation of general and commonly used proper nouns. For instance, in the article written by Thierry Meyssan, the term "Republican Congress" was translated into "الكونغرس" and it was taken as a proper noun where the first letter of each word is capitalized. Another example on that is the term "United Nations" which was translated directly into "الامم المثحدة". This shows the importance of the mobile translation in terms of accuracy in translating proper and widely known official terms. B- Refutation While many commercial mobile translation options are effective, they often cannot provide the capabilities or security levels that media agencies require. Most text-based mobile machine translation solutions call out to unsecured servers in the cloud over unencrypted phone lines, a serious issue for translating sensitive data. However, the most important challenge facing the mobile translation industry is the linguistic and communicative quality of the translations, although some providers claim to have achieved an accuracy in "understanding" idioms and slang language, machine translation is still distinctly of lower quality than human translation and should be used with care if the matters translated require correctness. III. Recommendation After viewing and researching the above points, on the different and common points between these two very competitive types of translation, the following can be recommended: • Do not utilize without a post-editing stage • Always remember the customer not the budget • A translator should also be objective in his translation so that his personal point of view will not influence the purpose of the main text. • Translators need to accept the new technologies and learn how to use them to their maximum potential as a means to increased productivity and quality improvement. • Utilizing TM software provides users with quick and easy access to a wide array of formats without the need for ownership of the software. • When users employ the likes of TM software for rather large projects, the undertaking is made easier by way of increased savings(time, expense, energy). Thus, there may or may not be a need to translate subsequent versions of large projects. • Users must update their mobile translation software as soon as a new update appears. • Provide more classes on the relations between machine and human translation and more training sessions on the use of the different translation software. IV. Conclusion After comparing the human translated documents with that of the machine, we can deduce that any attempt to replace Human Translation would certainly face failure due to a simple reason; there is no machine translation that is capable of interpreting, beautifying and making the text easy to understand while translating. For instance, it is only the human translator who is able of interpreting certain cultural components that may exist in the source text and that cannot be translated in terms of equivalent terms, just like what automatic translation does, into the language of the target text. In addition, it is widely agreed upon that one of the most difficult tasks in the act of translation is how to keep the same effect left by the source text in the target text. Machine translation, in this regard, has proved its weakness when compared with a human translation. The human translator is the only subject in a position to understand the different cultural, linguistic and semantic factors contributing to leaving the same effect that is left in the source text, in the target text. V. References Shwartz, K. (2013). Fed Tech Magazine. Retrieved from http://www.fedtechmagazine.com. Hermen, J. (2011). Mobile Translation and Its Advantages. Retrieved from http://justinhermen.blogspot.com

Legal translation is the translation of texts within the field of law

I- Abstract Hamade, K., Mohamad, M., Hashem, R., and Farhat, A. (2013), in their " MT a Gift from Heaven to Legal Translators", contains the research and analysis of the common and different points with respect to legal documents. It diagnosis the faults of these documents by human and machine. Meanwhile, the flaws and translation problems are categorized and analyzed, and recommendations are given. Thus, this paper indicates clear linguistic problems in addition to common recommendations as a result to such factors with a slant towards MT's many benefits in this specific category. II- Commentary Legal translation is the translation of texts within the field of law. As law is a culture- dependent subject field, the work of legal translation and its products are not necessarily linguistically transparent. Therefore, translation of such documents has positives and negative aspects that are necessary for a proper, well organized, and meaningful translation. A-Endorsement As legal documents are a case in which time and efficiency greatly matter, the evolved machine translation has played a major role. Cost-effectiveness aside, one of the strongest elements to machine translation is speed. Machine translation allows you to upload multilingual content almost instantly. The addition of translation memories and glossaries into a machine translation package are of great benefit in this category. The premise is still a translation engine like Google but it is now layered with bespoke translations which are specific to holding on to specific forms of commonly used legal documents. These systems can be built into pre-existing translation tools which can then merge with a post-editing system. Essentially, it’s making the hilarious miss-translations a thing of the past. There is much more impressive tech stuff on MT. As seen in the legal document "Apartment Lease Contract" saving such templates is essential for time and word choice as seen in the document more than 50% of the machine translation is similar to that of the human in just a third of the time. For example: "residential unit" was translated by human and by machine as "وحدة سكنية " , which is an overused word by those in the legal field. Google utilizes a majority wins situation where the most common translation across the web is used as translation. Equally, it’s almost tried and tested content with the ‘one-person opinion’ translation over-ruled by the masses which will ultimately be your customer base. Thus this doesn't mean that machine translation should always win over the traditional use of humans. Grammar wise, there are no major mistakes in the translation of Google regarding plural, singular, or verb tenses. However, techniques by humans conquer that of the machine when it comes to proper nouns etc.. which is a major editorial in legal translation. For example, "mister Tarek" was translated correctly as السيد طارق . B- Refutations Machine translation never flows as well as human translation. Despite its evolution, it will still translate the words rather than the concept you are trying to put across. For instance, the in first legal document, when human translated, the translator used various transitional words to ensure the flow of ideas throughout the writing, whereas, in the machine translated, not even a single conjunction or transition was used. For example, the second paragraph in the machine translated legal document started with:"Signed the United Nations", the third paragraph began with:"The General Assembly", as for the fourth paragraph:"The amendment". This shows that machine translation doesn’t give the proper synchronism needed in the article. If a customer searches on Google and finds your company, the first engagement with your company would be tainted by off-message copy and literal translation rather than your proper legal commitment. And it’s also important to remember that customers don’t just enter via your homepage so this is something you should be thinking about at any point of your site. Keywords should always be researched rather than translated to find what local customers are actually searching for. Don’t make the mistake of thinking that the only difference between the legal systems is language! These keywords need to be woven into your content both in meta-data and in-content itself. III- Recommendations In according to the following points much can be advised, especially when it comes to human translators, including: • Integrating MT work into your own for a quicker translation • Don't forget to not completely depend on the machine • Always remember the customer not the budget • Additional training courses for beginners in Legal translation software • Save commonly used templates into your system for a more efficient result IV- Conclusion There are hundreds of pros and cons for both machine translation and human translation approach. Ultimately it comes down to defining factors such as content use, target audience, sector etc. A good mixture of both generally creates an effective multilingual website which doesn’t cost the earth. But content is not one of the areas you always want to scrimp on.

Wednesday, November 20, 2013

Human VS Machine Translation

Presented by : -Amani carolina Yehya -Douaa Al Ayash -Ramona Shaaban - Amina Al Ashkar - Nivine El Banna I – Abstract This paper compares the human translation with the machine translation. It studies the different aspects of sentences’ structure that are: semantics, syntax, morphology and comprehension. It realizes the big difference in both the meaning and the purpose behind each text. After applying this analysis, results can be clear that no machine translation can result in a credible, meaningful and loyal translation to the source text. This is highly applicable in the legal translation. An immense difference in the quality of translation is obviously realized which led into an incomprehensible translation because of the weakness of the grammatical structure, the word choice, the word order and the lack of coherence. II. Commentary Translation studies have known the emergence of new methods of translation including the so-called Machine Translation. However, its emergence was not at the expense of Human Translation for the latter proved to be the only subject capable of translating not only by means of substituting words for words, like Machine Translation, but also in terms of respecting linguistic, semantic, and more importantly cultural differences between languages. Actually, before any translation, there should be a full understanding of the source text from the part of the human translator. A. Human VS Machine Translation 1. Analysis Why Human Translations are better than Machine Translations? The limitations of the most popular online translation tools are apparent, but there are more points to consider: • only humans can understand and effectively translate the cultural components of source text to target text. While machine translators can quickly produce target text from inputting source text, the machine does not recognize nor translate idioms, slang, or terms that do not appear in the machine’s memory. • Machine translations are often literal, or word-for-word translations, hence the errors and strange language that often appear. • Human translators can manipulate language in such a way that they mimic the style and purpose of the source text. For example, if the source text is an upbeat promotional piece, a human can reproduce that to create effective materials in the target language. Hatim, B. & Mason, I. (1997) It is conventionally believed that familiarity with the source and target languages, as well as the subject matter on the part of the translator is enough for a good translation. However, due to the findings in the field of text analysis, the role of text structure in translation now seems crucial. To compare and contrast between human translation and machine translation (Google) we must deal with six main parts of the text: semantics, morphology, syntax, mechanics, coherence and thematic links. 2. Morphology We often use final inflections to change an English word's grammatical characteristics, such as the number, tense or voice. English uses prefixing and affixing as the most popular methods of word formation. Arabic uses modeling which means creating words according to the models or patterns. Google translated the word لم يكن متاحا into unreachable. What we realize here is that in Arabic we added لم but in English we added a prefix which is "un". As for the French part then it was translated into inaccessible . In google , the past tense Arabic word اسس into established in English. An "ed" was added to clarify that this verb is in the past tense. This addition is related to the inflectional part of the language. Another word in الأجيال which was translated into generations by adding an s at the end to show that this noun is in its plural form and that’s also related to the inflectional part of the English language. 3. Syntax Classical Arabic tends to prefer the word order VSO (verb before subject) rather than SVO (subject before verb). Subject pronouns are normally omitted except for emphasis or when using a participle as a verb (participles are not marked for person). Auxiliary verbs precede main verbs, and prepositions precede their objects. In human translation, the syntax is well done. However, the Google translation contains syntax mistakes. For example, the sentence “Both parties hereto have hereby agreed that the rental value of the dwelling unit subject to this Agreement shall be of the sum of L.E …” was translated by Google as the following: "كلا الطرفين لهذه الرسالة قد وافقت بموجب هذا أن القيمة الايجارية للوحدة السكنية الخاضعة لهذه الاتفاقية تخضع لل من مجموع جنيه ............... (ليرة فقط ........... المصرية ) ليتم دفعها شهريا ويكون زيادة من قبل ”. However, the translation done by human for this sentence is “اتفق الطرفان على أن تكون القيمة الايجارية الوحدة السكنية موضوع هذا العقد هي مبلغ جنيه (فقط جنيه) شهرياً تزاد بواقع % سنوياً في بداية السنة السنة الثانية . 4. Lexical English words have been traditionally classified into eight lexical categories or parts of speech (and are still done so in most dictionaries): • Noun: any abstract or concrete entity • Pronoun: any substitute for a noun or noun phrase • Adjective: any qualifier of a noun • Verb: any action or state of being • Adverb: any qualifier of an adjective, verb, or other adverb • Preposition: any establisher of relation and syntactic context • Conjunction: any syntactic connector • Interjection: any emotional greeting (or "exclamation") This category contains Arabic parts of speech: Grammatical functions of Arabic words. • Category: Arabic adjectives: Arabic words that give attributes to nouns, extending their definitions. • Category: Arabic adverbs: Arabic words that modify clauses, sentences and phrases directly. • Category: Arabic articles: Arabic words that indicate and specify nouns. • Category: Arabic conjunctions: Arabic words that connect words, phrases or clauses together. Grammar: The example of “human translation” mentioned before is also a fault in grammar. But in the other hand, there are no major mistakes in the translation of Google regarding plural, singular, or verb tenses. However, verb tenses have to be chosen in way that preserves the meaning of the sentence. So from grammatical point of view the sentences translated by Google may be correct, but the meaning may differ if the verb tense is not well chosen. This case may be found in literary text which contains the variety of verb tenses, and, usually, it is not the case in journalistic texts. Word choice: In word choice, Google didn’t choose the accurate word like in the human translation. Names and family names: Sometimes Google translation fails to give the name or the family name of an author, politician, etc … Coherence: Sometimes there is no coherence at all in a text in Arabic already translated from English or French. It is a good example to know how much Google translation may be like a “collection of word” not a sentence well cohered. In the given above machine translated text we see the inappropriate transition from one sentence to another. The relative shortness of the text makes it easier for the machine to translate it and keep the overall meaning coherent, but even in such short texts it is essential to keep the structure and conjunctions that deliver the right meaning. In Arabic language we do not start a new sentence with an adjective or an adverb or a noun, the right structure is: subject- verb- object- complement. Any wrong use of words and transitions makes the text incoherent and hard to follow the meaning. III. Recommendations Translators should recognize and learn to exploit the potential of the new technologies to help them to be more rigorous, consistent and productive without feeling threatened. Some people ask if the new technologies have created a new profession. It could be claimed that the resources available to the translator through information technology imply a change in the relationship between the translator and the text, that is to say, a new way of translating, but this does not mean that the result is a new profession. However, there is clearly the development of new capabilities, which leads us to point out a number of essential aspects of the current situation. Translating with the help of the computer is definitely not the same as working exclusively on paper and with paper products such as conventional dictionaries, because computer tools provide us with a relationship to the text which is much more flexible than a purely lineal reading. Furthermore, the Internet with its universal access to information and instant communication between users has created a physical and geographical freedom for translators that were inconceivable in the past. We share the conviction that translation has not become a new profession, but the changes are here to stay and will continue to evolve. Translators need to accept the new technologies and learn how to use them to their maximum potential as a means to increased productivity and quality improvement. IV. Conclusion Any attempt to replace Human Translation totally by machine translation would certainly face failure for, due to a simple reason, there is no machine translation that is capable of interpretation. For instance, it is only the human translator who is able of interpreting certain cultural components that may exist in the source text and that can not be translated in terms of equivalent terms, just like what automatic translation does, into the language of the target text.

Recent Trends in Machine Translation”

Written by: Abir HASSSAN- Bassima KHALED- Rania FOUANI I. Abstract Miller (2008) in his article titled “Recent Trends in Machine Translation” indicated that In the last two years machine translation (MT) has embarked on a voyage into the future, spurred by the presence of personal computers on individual desktops throughout the world and, more recently, universal access to electronic text on-line. This impressive growth has led to many new trends, including major changes in the profile of the user. Apace with this trajectory has come better communication and increased collaboration between all the groups concerned—MT researchers, developers, users, and watchers. The International Association for Machine Translation (IAMT), together with its three regional associations created in 1991, has fostered this convergence by creating opportunities-workshops, conferences, publications—through which to share the latest information in this dynamically growing field. II. Commentary A. New Dimensions in MT Service Delivery a. The dreams of yesteryear's visionaries are finally coming true. Machine translation (MT) has launched on an unparalleled surge of growth—a historic shift in the way it is being used and a phenomenal increase in the number of people who rely on it. We now have MT software that is viable, affordable, and runs on virtually any 1990s desktop. Today there are more than 500 vendors of MT software for the personal computer around the world, and among them they put out well over 1,000 products.3 One of the vendors, Global link, sells its extensive line of software in at least 6,000 stores in North America alone, and at present Europe is its fastest-growing market. The ubiquity of the desktop computer with access to the Internet has given momentum to an unprecedented growth in MT user ship. We now have MT on-line, accessible through e-mail, client server arrangements, Internet service providers, and a growing number of other sites on the Internet. The on-line phenomenon is changing our whole way of thinking about machine translation. Together, these two developments-the abundance of low-cost MT in shrink-wrapped boxes, coupled with MT on-line—are turning machine translation into an everyday commodity that is within the reach of virtually anyone with a late-model personal computer. The sudden shift in MT use and the dramatic increase in its user ship have also brought a sea change in the profile of the user. Because of its widespread availability, MT has been forced, appropriately or not, to graduate from the days when a system's caretakers had to nurture it constantly in order for it to perform acceptably. It now stands on its own, and, by and large, its new users must fend for themselves, whether by customizing the system and/or learning how to post edit, or accepting the output as it is. While all these changes are taking place in MT use, other exciting trends are also redrawing the entire map of the field of machine translation itself. Many languages, especially the more challenging ones, are being tackled and added to the vendors' repertoires. In the new game of "plug-and-play," MT engines are now being made inter faceable with a variety of other software functions. Speech translation is making steady progress. Off-the-shelf tools are speeding up research and development. Creative partnerships are being forged between and within the commercial and academic communities. Systems of different philosophies are being joined together. Indeed, on all fronts MT research is accelerating its ongoing march toward distant horizons. It's safe to say that never in the history of this field has so much happened within such a short period. b. We have all witnessed the explosive expansion of the World Wide Web, the Internet service providers, and, most recently, the intranets. Not many of us, however, are aware of the extent to which machine translation is being swept along in this tide. Already on-line access is causing MT use to grow at an unprecedented rate. As of September 1996, low-cost machine translation in one form or another was available at some 30 on-line sites in cyberspace. It comes in a variety of forms and modalities. MT vendors are also currently gearing up for intranets. On-line purchase is yet another way to go. As with many other kinds of software, the vendors make it easy to order an MT package on- line. In fact, we predict that within a few years the shrink- wrapped box will have yielded almost entirely to on-line sale/purchase arrangements. This growing use of MT on-line cannot be dismissed as casual curiosity. Unlike software purchased off the shelf, for which no direct measurements are possible, on-line access is documented automatically, and therefore patterns can be discerned. For example, the records for CompuServe's production translation service show a number of repeat large-volume users. The statistics (ibid.) reveal that about 85% of the requests are for raw MT—a much larger percentage than had been anticipated. What could not be determined automatically was whether the raw translation was being used for gisting purposes only or whether it was being post edited for further use. To discover more about its subsequent fate, Flanagan conducted a market survey which revealed that the CDTS is used mostly for business and technical purposes where assimilation- quality MT is sufficient (ibid.). The bottom line is that the customer is willing to pay for this service. In the World Community Forum, although there is no direct evidence of the extent to which the machine translations are being relied on, at least one fact can be reported: the Forum's sysop is inundated with complaints on the rare occasions when the MT system goes down. In these circumstances, only a fully automatic process capable of handling very large volumes of text with near-real-time turnaround can provide the translation capacity required by on-line markets. Flanagan (ibid.) also points out that the on-line culture favors rapid and shallow assimilation of information. For these reasons, MT is an ideal fit. B. The New User/Consumer Profile Now that we have seen the new trends in MT from the point of view of the general public, we should look at the perspective of the user and the end consumer. 1. Acceptance: Purpose of translation (a new typology). Traditionally MT usage has been classified according to its purpose. It is considered to be either translation for dissemination, or translation for information purposes only, also known as "gisting" or assimilation. At this point we would like to add a third category and at least two types of each. Type 1 represents the more direct use of MT in one of its natural niches, while type 2 is a further development that requires greater human intervention at some point in the process. Problems: Heavy post editing, judgment calls are time- consuming, domain drift, hence need for improved quality; few systems perform well in this arena; linguistic development investment difficult to target. What it takes: post editing aids; very large and sensitively coded lexicon(s), easy to update (better a combined dictionary than "topical glossaries"); parser and rule base; filters and translation memory also helpful. 2. Assimilation: a. "Raw" MT for gisting, sometimes automated post editing; broad range of subjects. Problems: Quality tends to be poor. What it takes: Very large and judiciously coded lexicon(s), easy to update (better a combined dictionary than "topical glossaries"); parser and rule base. b. Problems: Lack of public awareness of this option; shortage of suitable post editors, translators often not able to relax standards. What it takes: Good quality; large and richly coded dictionaries. III. Conclusion and Recommendation To sum up, MT Service Delivery has new dimensions where the machine translation launched on an unparalleled surge of growth. In addition to a new User/ Consumer Profile. It is essential for a translator to know that as high-level executives begin to see the huge value and market enabling power of translating large amounts of relevant content, we can expect to see that translation will be viewed as a much more strategic core competence. As this happens, translation professionals could become facilitators and enablers of many key conversations between global enterprises and their customers. The skills required will include the following (and many are just emerging so this is a great opportunity for innovators and leaders): 1. customization of MT systems for specific business purposes; 2. corpus analysis and assessment skills; 3. evolutionary approaches to making high value content multilingual; 4. rapid quality assessment skills; 5. linguistic steering of automated translation systems; 6. community and crowd collaboration management and administration to do a variety of linguistic work; 7. more structured approaches to post-editing MT to enable rapid error identification and correction; 8. continuously evolving and learning MT systems that produce on-going improvements in translation quality; 9. much better and more robust data interchange standards will likely develop Systran: Since September 11, 2001, the warlike spirit which blows on Washington seems to have swept these scruples. Reverso: Since September 11, 2001, the warlike spirit which blows on Washington seem to have swept (annihilated) these scruples. Human translation: Since 11 September 2001 the warmongering mood in Washington seems to have swept away such scruples. VI. References Anoun, H. (2006). Towards a Logical Approach to Nominal Sentences Analysis in Standard Arabic. In Proceedings of the Eleventh ESSLLI Student Session. Badawi, A. Elsaid, L. Mike, and G. Carter, G. (2004). Modern Written Arabic: A Compre-hensive Grammar. Routledge. Baerman, M. et al (2006). the Syntax-Morphology Interface. A Study of Syncretism. Cambridge Studies in Linguistics. Cambridge University Press. Bar, H. and Yoad, W. (2005). Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages. Ann Arbor, Michigan: Association for Computational Linguistics. (pp. 39, 46) Bar, H. and Yoad, W. (2005). Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages.

Machine Translation (Google) and Human Translation

Abir Zein Zeinab Kabalan Lea Haj Hassan Nabila Wehbe Abstract Translation in the Arab world, for instance, is known as "an act of understanding before explaining". In this regard, it is necessary that before starting the translation of any text, the translator should have a clear understanding, linguistically, semantically and culturally speaking, of that source text so that he or she would be able to convey the real intended meaning of the target language. This paper is an attempt to draw a distinction between Machine Translation and Human Translation shedding light on the different characteristics of each one. Thus, for the sake of illustrating, it will provide many legal texts that are translated by both Machine Translation (Google) and Human Translation. Analysis: It is quite obvious, from the first reading of each translation, that machine translation is not that perfect rendering of the source text into the target text. The point is that the translated text, still, bears much of the traits characterizing the language of the source text; therefore, much should be said about how the use of language is violated as well as the meaning. 1. Semantics: The source text (sample 1): call today for more information! Human translation: أجر الطرف الأول المؤجر للطرف الثاني المستأجر القابل لذلك الوحدة السكنية الموضحة المعالم بالبند التمهيدي Machine translation الطرف الأول، والمؤجر لم تسمح بهذا على الطرف الثاني واستأجرت الطرف الثاني بموجب هذا الاتفاق من المؤجر الوحدة السكنية التي يتم الإشارة إلى الميزات في In the above example, the machine translation is a literal translation or instead a word-for-word translation; the reader can easily notice that there is no flexibility in the machine translation in that each word in the source text has been substituted orderly by another in the machine translation.Thus, it becomes clear that machine translation, is a translation, the focus of which is the source text rather than the target text. The word order is respected only in the source text. 2. Morphology: . Human translation يقر الطرف الثاني المستأجر أنه قد عاين العين المؤجرة المعاينة التامة النافية للجهالة ،وقبل استئجارها بالحالة التي هي عليها ،ويقر أن الوحدة السكنية صالحة للغرض الذي أستؤجرت من أجله ويتعهد بأن يستخدمها فيما حدد لها Machine translation: يجب أن الطرف الثاني أن تعلن اضطلعت الطرف الثاني التفتيش السليم للمباني المؤجرة وقبلت لاستئجار كما هو ويقر بأن الوحدة السكنية ليناسب الغرض الذي تم تأجيره عليه ويتعهد استخدام وحدة حسب ما تم تعيينه. Although the meaning can be comprehensible; nevertheless, the structure of languages are different and, hence, they should be respected for the sake of producing a well-formed translation in the target language. The inability of the machine translation to produce a well-structured text is due to its focus on the "comprehension" and not "the production of a perfect target text". So far as the human translation is concerned, the above example can reveal, clearly how the human translator is capable of avoiding what have been criticized in the machine translation. The human version is a structure respecting and its focus has been in both the source text, in an act of comprehension, and the target text, in an act of producing a perfect translation. The human translator's flexibility allows them to move from language into another bearing in their minds the difference of structures between languages. 3. Syntax: No one can deny that the main rationale behind any translation is to transfer as much as possible the meaning intended by the source text's writer into the target text. Yet, in machine translation, this is not always the case in that sometimes the achieved meaning is ambiguous, distorted, and it becomes difficult to grasp it just like in the following example: The source text (sample 3): particle distribution curve and particle distribution analysis data are not output, and the output is confined to only the CBC 8 parameter. Machine translation: يجب على المستأجر القيام هيربي لدفع أي ضرائب والرسوم والمخالفات والتعويضات أو مصاريف أو الناتجة عن استخدام الوحدة السكنية الخاضعة لهذا الاتفاق من تاريخ توقيع هذا الاتفاق والمؤجر لا يتحمل أي منهم Human translation: يلتزم المستأجر بسداد أية ضرائب أو رسوم أو قيمة أية مخالفات أو غرامات أو تعويضات أو مصروفات تتصل بالوحدة موضوع هذا العقد أو تنتج عن استخدامه لها من تاريخ التوقيع على هذا العقد ولا يتحمل المؤجر بأي منها In this example, the machine translated sentence produces certain associations with no sense. This is mainly, as stated before, due to the fact that machine translation focuses on the source text's language which is in this case English, as being different from Arabic. As for the human translation in the same example, the ability of the translator to substitute the words renders the translation easy to be understood. it is only through human translation that the translator can add or delete certain words or even phrases, sometimes, for the sake of clarity. Conclusion: Finally, it is obvious that the machine translation can’t compete with the human capacity to produce and interpret words and languages, because the meanings are missed in many phases of translation also the translation is limited and can’t recognize nor translate some words like idioms and slang for example. On one hand, someone said that: “Machine translation is an important but difficult problem. One of the properties that makes it difficult is the fact that different languages express the same concepts in different orders. A machine translation system must therefore rearrange the source language concepts to produce a fluent translation in the target language.” On the other hand, human translators can reproduce different words to create effective materials in the target language. While the Machine translations are useful in giving the reader a general idea of what the source text says, but can never replace the human element in translation, and only a human translator can render a translation suitable for public consumption. Recommendations: It is recommended that translators with the help of specialists and expert to upload and update their memories for machine translation, regularly. It is recommended as well that translators pinpoint the weaknesses and mistakes of machine translation, in order to go back revise and check these points. Programmers have to stop creating programs that seek to replace human translators for the reasons mentioned above. And the programs that are made need to be of such a nature that human translators can make use of them to enhance the translation process and make it easier and more accurate, like it is being done with translation memories.

Trends of MT

Abstract Douaa Al ayash(2013) said that these days the most important cause of economic wealth is information and its access.This is a real state that we are in not only for the world,but for the countries that are willing to achieve good economical state and to improve their business.Here,it is important to stop at this point and take look at the cause behind the improvement of business related to professional translation.Machine translation is one of the technologies that are causing a better professional translation achieving knowledge an economic issues as well. Nowadays , we can find a huge encyclopedia spread all over the world,which is Internet.Information is spread in a large number and that encourages knowledge.Thus,it is very important to make a successful usage of this information to get a multilingual information.

Arabic Computational Linguistics

Abstract: Jana Issa(2013) said that a translation memory consists of text segments in a source language and their translations into one or more target languages. These segments can be blocks, paragraphs, sentences, or phrases. Individual words are handled by terminology bases and are not within the domain of TM. In this document we indicate the using of Translation Memories (TM), its history, and the benefits. We can mention only the most significant research systems and projects and only the most important operating and commercial systems.

New Trends of Machine Translation

I. Abstract Miller (2010) stressed in his article on the current machine translation systems. The author explained the components of a machine translation system from the standpoint of software, linguistic components, and users' demands. The importance of pre-editing and post-editing is stressed. The semantic and contextual processings are essential to obtain a better translation quality, which are the future problems to attack. Attention is given to the difficulty of contemplating a pivot method in machine translation instead of transfer methods, because the projection from a word or a phrase to a concept is very difficult if we want to have a very exact concept representation and translation. A new transfer method which accompanies the pe-transfer structural adjustment and post-transfer adjustment is explained... Systems always are imperfect, and users must use them after recognizing the possibilities and the limitations of the system, that’s why various trends in this domain will be discussed in this notation. Nivine El Banna

Computer-Assisted Translation

Computer-Assisted Translation aims at presenting the specificity of computer-assisted translation and to initiate students to the use of the main MT tools .